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Thermal computational analysis of microclimates for optimal crop production in controlled atmosphere
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Energy (2019) 000–000 783–790 EnergyProcedia Procedia160 00 (2017) www.elsevier.com/locate/procedia 2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, Sydney, Australia 2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, Sydney, Australia
Thermal computational analysis of microclimates for optimal crop production in controlled atmosphere Thermal computational analysis of microclimates forCooling optimal crop The 15th International Symposium on District Heating and production controlled atmosphere a b, Mohammed AbrarinSharief , Ashfaque Chowdhury * Assessing the feasibility of using the heat demand-outdoor a University, Spencer Street, Melbourne, b, Vic 3000, Australia Schoool of Engineering and Technology, CentralSharief Queensland Mohammed Abrar , Ashfaque Chowdhury * Qld 4680, Australia temperature function aQueensland long-term heat demand forecast Schoool of Engineering and Technology,for Central University, district Bryan Jordan Drive, Gladstone, a
b
b
a
a Schoool of Engineering and Technology, Central Queensland University, Spencer Street, Melbourne, Vic 3000, Australia a,b,c a a b c c Schoool of Engineering and Technology, Central Queensland University, Bryan Jordan Drive, Gladstone, Qld 4680, Australia
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
This study aims to perform a thermal computational analysis of the indoor environment with a view to identifying a promising Abstract method to improve the level of harvest in a regulated indoor space or modified version of controlled environment agriculture (CEA) space.study A modified cultivated greenhouse modelanalysis is considered a customary with model. The operational environment of This aims toclosed perform a thermal computational of thebased indooronenvironment a view to identifying a promising Abstract the refined CEA space modelofisharvest simulated computational fluid dynamics environment. In the study, CEA spaceagriculture is assumed(CEA) to be method to improve the level in a in regulated indoor space or modified version of controlled environment heated Abymodified a constant heatcultivated source from the samemodel path for a limited time. space has twoThe air operational inlets on one side and two space. closed greenhouse is considered basedThe on aCEA customary model. environment of District networks are commonly in the literature as one of the most effective solutions for decreasing the outlets on heating the other side. A standard k-epsilon model was implemented in environment. the simulation to define the turbulent transport the refined CEA space model is simulated inaddressed computational fluid dynamics Inmodel the study, CEA space is assumed to be greenhouse gas the emissions from the sector. systems require investments which are returned thetwo heat phenomena. On virtual environment, crop defined usingtime. a porous medium that was proposed byand Darcyheated by a constant heat source frombuilding the the same pathisThese for a limited Thehigh CEA spaceapproach has two air inlets on onethrough side sales. on Due toother theThe changed climate conditions and was building renovation policies, heat model demand in the the future could transport decrease, Forchheimer results are recorded in the form of temperature, velocity and pressure distribution. outlets theLaw. side. A standard k-epsilon model implemented in the simulation to define turbulent prolonging the investment return period. phenomena. On the virtual environment, the crop is defined using a porous medium approach that was proposed by DarcyThe main scope thisresults paper are is torecorded assess the feasibility using the heat demand outdoor distribution. temperature function for heat demand Forchheimer Law.ofThe in the form ofof temperature, velocity and–pressure districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 ©forecast. 2018 TheThe Authors. by Elsevier ©buildings 2019 The Authors. Published by Elsevier Ltd. and typology. that vary in both under construction Three weather scenarios (low, medium, high) and three district This is an open access article the CC period BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) renovation scenarios were developed (shallow, intermediate, deep). To estimate the International error, obtained heat demand values and were Selection andAuthors. peer-review underbyresponsibility of the 2nd Conference on Energy © 2018 The Published Elsevier Ltd. of the scientific committee Selection and peer-review under responsibility of themodel, scientific committee of the and 2ndvalidated International Conference on Energy and compared with results from a dynamic heat demand previously developed by the authors. Power, ICEP2018. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Power, ICEP2018. The results that when weather change considered, the margin of error could be acceptable for some applications Selection andshowed peer-review underonly responsibility of theisscientific committee of the 2nd International Conference on Energy and (the error in annual demand was lower than Analysis, 20% for all weather scenarios considered). However, after introducing renovation Keywords: Crop Production, Microclimate, Thermal Power, ICEP2018. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of Production, slope coefficient increased on Analysis, average within the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Crop Microclimate, Thermal decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. by Elsevier * Corresponding author. Published Tel.: +61411767 886. Ltd. Peer-review under responsibility of the E-mail address: [email protected] Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +61411767 886.
1876-6102 © 2018 The Authors. Published by Elsevier Ltd. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND Selection and peer-review under responsibility of the scientific 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. committee of the 2nd International Conference on Energy and Power, ICEP2018. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 10.1016/j.egypro.2019.02.160
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1. Introduction Optimum growth of agricultural product is an incredibly important aspect in the existence of all living lives. The crop production and distribution play a significant role in the economic development of many countries. Therefore, crop production should be maintained as efficiently as practical so that the ever-increasing needs and demands can be fulfilled. Apart from its diverse economy and ecology, Australian farmers are also challenged by the diverse weather conditions. Such variation in weather can sometimes act as a significant problem in the farming because some harvests are very delicate to any changes in atmosphere and thus effects in degradation of the crop quality. Cultivation with the help of greenhouses can be an option to overcome such problems. In the past, greenhouses were just protective glass houses made to trap the sunlight to maintain the indoor temperature. Greenhouses now are equipped with other facilities such as temperature control, light control, carbondioxide control, relative humidity, etc. Adjustments and optimizations are vital in greenhouses to conquer an enhanced plant growth. Numerous resources are put to the test to study the heat transfer properties. Changes in the parameters such as temperature, water vapour, air pressure, air velocity, the rate of radiation etc. have a severe influence on the plant development rates and overall quality of the crops [1]. Therefore, greenhouses or controlled environment agriculture (CEA) spaces are incredibly critical in contemporary agriculture industries. The process of performing a computer simulation of the airflow phenomena in a greenhouse is not only user-friendly but also are very versatile and accurate to understand the controllable factors in the greenhouse. Such an analysis can be performed before the actual greenhouse is constructed or can be applied to an existing greenhouse for retrofitting. The outcome of the study allows the user to identify the optimum design and operating conditions in a cost-effective way. 2. Computational Analysis of Greenhouses Greenhouses are considered as a significant infrastructure which can enhance the crop production and to protect the crop from any forms of dents from weather conditions, pests and rodents etc. Modern CFD programs allow more straightforward investigations of the scalars and vector fields within the greenhouse through the determination of the transport equations of the air that reigns the ventilation [2]. Ventilation is an important consideration during the construction stage of a greenhouse as the ventilation acts as a means of heat and mass transfer to maintain the flow of air into and out of the greenhouse. The adequate level of ventilation helps to dissipate excessive heat, to enhance the exchange of carbon dioxide and oxygen and to maintain acceptable humidity levels since these factors affect the crop development and production. Ventilation is thus a critical process that strongly influences crop production at every stage of the process. Since its very complex to characterize adequate ventilation physically, the computational fluid dynamics (CFDs) procedures are used to map the design and create a better understanding of the air flow and creation of microclimates [3]. It can also be noticed that side openings are less effective than roof openings and adding a side wall may significantly increase greenhouse ventilation [3]. Moreover, ventilation rate strongly decreases as the number of spans increases in a combination of both roof and side wall opening. In a study l2] reported that the CFD method is very effective means of determining the influence of temperature and wind on the crop growth in greenhouses as well as in other viticulture buildings. The researchers applied four different air velocities and found that the ventilation was influenced strongly by the presence of small vent opening area. Insect proof nets have been progressively accepted in protected horticulture industries. Precisely designed superfine meshes are adopted to keep the pests away without the use of harsh chemicals. CFD analysis has been gradually taken to investigate greenhouse aeriation driven by either wind or buoyancy forces. Fatnassi [4] used tomato crop in a largescale greenhouse which was then fitted with the insect-proof screens and was then simulated using the CFD software which analyses water vapour, heat and mass exchanges between the air and the crop, soil temperatures etc. The comparison between measured and computed values for air exchange rate found relatively good accuracy of the model and its ability to create a realistic impression of the greenhouse. The simulations successfully predict the indoor airflow, temperature and humidity with significant temperature and humidity increase with the screen. The results recommended increasing the vent area in order to reserve suitable climatic situations when insect-proof screens are adopted [4].
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Natural ventilation is broadly utilized in Greenhouses in Mediterranean climate as such type of ventilation consumes less energy, and spent less money to operate and maintain the setup. High-level humidity during winter influences the crop production due to fungal production and diseases. High humidity causes condensation on plant surfaces which enhances spore growth of certain pathogenic fungi. These fungi reduce in number as the moisture reduces. The high humidity also leads to leaf necrosis, calcium deficiencies and soft, thin leaves which eventually reduce the crop growth [5]. In Almeria province of Spain, a favourite type of greenhouse called Almeria-type is in use where natural ventilation is adopted as the primary climate control method. Natural ventilation consumes less energy, minimum operation and maintenance support and cheaper than other systems to control the indoor temperature. In summer, this type of greenhouse in employed to increase air humidity and to lower the temperatures whereas in winter this type of greenhouse reduces the moisture. The dynamic potency of natural ventilation is the combined effects of wind and buoyancy where its relative importance depends on wind speed and the temperature differences between the inside and outside of the greenhouse. Canary or Parral type of greenhouses are generally extensive (approximate 1 hectare or more), as a result, the side wall ventilation is preferably employed than the roof ventilation [6]. It was reported that Parral type greenhouses fail to consider the interaction among the canopy, the air and the radiative exchangers. When a fine mesh net was employed to avoid the insects, the functioning of ventilation was reduced due to the existence of insect screens. Majdoubi [6] concluded that basic understanding of air exchange mechanisms is essential in determining the suitable climate for the greenhouse air and the canopy. The orientation of crop is also a crucial factor information of a microclimate. Greenhouses require a continuous supply of energy from either renewable or non-renewable sources to maintain the internal microclimate with parameters such as air temperature, wind speed, light intensity etc. Using the CFD technique, Chen [7] investigated the control method integrated with the heat transfer mechanism in a greenhouse considering the impacts of canopy, insect proof screens and heating systems for a perfect microclimate and plant activity. However, heating control strategies for the greenhouse was not adequately considered in the CFD environment; therefore, the optimal design aspects were not completely satisfied in the demand of control system which could improve the indoor temperature conditions and warming efficacy. Al-Helal [8] proposed a thermal model based on pure energy balance applied to a control volume within a greenhouse. The model was applied to predict the natural ventilation rate of any type of greenhouses, for instance, transparent or canopy covered with any shapes and sizes at any locations. The model was simplified by avoiding any uncertain parameters such as average transmittance of the cover and thermal inertia of the soil because they would cause errors in the final results. It was found that the internal and external air temperature are the main parameters controlling the ventilation rate. The model reportedly worked well with zero solar radiation, i.e. at night. This study adopted the mathematical models proposed by Al-Helal [8] to model a modified greenhouse. Thermal computational analysis was also performed, and related parameters were computed in due time. The outcome of the study will assist in determining any change in the settings from the past years and to check if any improvement has been made in the newly analyzed model. 3. Methodology and Numerical Approach To perform the computational analysis, the controlled environment agriculture (CEA) space was first modelled in AutoCAD (Fig.1) and then imported to SimScale, a cloud-based simulation platform, to undertake the CFD analysis. The selected harvest was Australian native tomato which requires a temperature range of 15 deg C to 32 deg C in the CEA atmosphere for yielding at the optimum level. Since a good proportion of tomato consist of water, moisture is considered an essential factor during cultivation. The ideal humidity for tomato crop is between ranges of 80% - 90%. Unwarranted humidity causes suffocation to the crop and may damage the crop heavily. A reduced amount of humidity may reduce the quality of the plant, fail to self-pollinate and grow in the long run. Transpiration is also important in shifting water molecules between the air and the plant. A. Controlled Environment Agriculture Space Characteristics The CEA space is considered as a traditional type with four openings, two on each side. The area of the space is taken as 30 cm in height, 40 cm wide and 51cm in length. The air flow direction related to the sides was in one
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direction. The chosen crop tomatoes were assumed to be sprayed using the drip irrigation technique. The openings are assumed to be fitted with insect-proof screens in the selected controlled environment agriculture space. The CEA space has a total of 4 vents out of which two are present on each side, and the other two are present on the roof.
Fig. 1. The wireframe structure of the controlled environment agriculture (CEA) space
B. System Governing Equations The k- ϵ model is adopted to simulate the airflow in the greenhouse atmosphere. The CFD platform uses standard equations to affect mean flow characteristics, where k is the turbulent kinetic energy and ϵ is turbulent dissipation. The controlled environment agriculture space is modelled using the polyline command functions. The structure is then constructed and extruded in 2 dimensions mode. The main assembly is then hidden to model the rectangular plant structures. The geometry is then imported to SimScale by translating into an Initial Graphics Exchange Specification (IGES) model which ensures better compatibility with the platform. The velocity field, water vapour content and the associated temperature field were determined from the resolution of the popular mass, momentum and energy balance equations using the Boussinesq Hypothesis. The corresponding equations were resolved using the SimScale simulation platform. The presence of crop prompts a drag influence in the controlled space. This is incorporated in the CFD platform using a porous medium. The source termis governed by the Darcy–Forchheimer equation. The crop geometry was considered using the blocks of porous medium with same length and width with the leaf area index, ILA 3. The Darcy– Forchheimer parameters are quantified based on the porosity of the insect-proof net following the relations as given in equations Miguel [9]. The platform allowed to obtain the temperature, pressure and air velocity features in a converged solution. C. Mesh, Grid and Boundary Conditions The computational area considered in the analysis was restricted to the controlled environment agriculture space itself and the free space surrounding it. As shown in Fig. 2 and 3, a hexagonal dominant parametric mesh was created using the default values after importing the geometry into the software, SimScale. Thermal boundary settings were employed to all the walls. The air velocity inlet periphery condition is applied to the two of the openings of the controlled space, and the pressure outlet boundary condition is assigned to the remaining two openings of the same. The doors and other openings were assumed to be closed. The plants were replicated using a Cartesian box boundary condition which is adapted to simulate a porous medium inside the greenhouse. Selected periods of measurements were considered 2 h before and after midday in sunny summer conditions in reasonably steady state conditions with high radiation and ventilation rates. Radiation quantities inside the greenhouse were mainly considered to perform the crop sensibility and latent heat balance.
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Fig. 2. The Hex-Dominant Parametric mesh of the controlled environment agriculture space
Fig. 3. The Hex-Dominant Parametric mesh as a wireframe model showing fine structure near plants of the controlled space
4. Results and discussion The controlled atmosphere is considered as heterogeneous where different regions may have different microclimates. In general, the velocity of air reduces as it appears in the space in an enclosed structure, but the air is circulated throughout the enclosed area. Similarly, the pressure reduces as it appears at the centre of the controlled space due to the friction of air-fluid between the walls and plant structures. For simplicity, the air velocity was kept constant at 4 m/s during the simulation. The initial boundary conditions and mesh analysis were performed on a model space and results were recorded. The simulation was then repeated using the controlled space without the harvest and simulation was completed again. The final simulation was conducted in the model by adding the plant structure. The air path was established from the positive x-axis. As shown in Fig 4 and 5, an almost uniform temperature can be observed towards the centre of the greenhouse where the plants are placed. As can be seen in the simulation, the air appears in the space from both the entrances and then the air is circulated throughout the controlled space in Fig. 6. The loss of air velocity and pressure can be viewed due to the friction around the walls. Moreover, high-pressure values can be viewed at the top of the greenhouse as shown in Fig 7. The air escapes from the space via the remaining
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two exits in the direction of the x-axis. The temperature gradually rises near the centre of the controlled space where the plants were placed. The walls of the space remained at the initial temperature. A minor temperature difference was observed between the entrances and exits of the space. The least heated area was the top of the controlled space. The velocity and temperature distribution were found to be homogenous in the centre which is optimum for the growth of the crop. Any losses in the air velocity and momentum could be considered as the losses due to the friction factor of the walls and the plant structures. The results were fairly in agreement with the previous study reported in the literature.
Fig 4. The temperature concentration inside the model space with plants
Fig 5. the temperature concentration in the model space, with plants, in a wireframe structure
Mohammed Abrar Sharief et al. / Energy Procedia 160 (2019) 783–790 Mohammed Abrar Sharief et al / Energy Procedia 00 (2018) 000–000
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Fig 6. The air velocity and flow direction from both the openings in the model space with plants
Fig 7. The representation of pressure distribution in the model space
5. Conclusions This study assists in observing and studying the airflow phenomena which is useful for designing a new controlled environment agriculture space and/or retrofitting an existing one. The outcome of this simulation-based study is that the temperature was consistent towards the centre of the controlled environment agriculture space, which is usually achieved by using additional heating elements such as fans, hot water pipes etc. It was observed that the air flow circulation was interrupted as soon as it entered the space and decreased gradually which eventually caused a pressure difference. Further investigation is recommended to achieve a better uniformity in the airflow characteristics and enhance the overall efficiency of the controlled environment agriculture space for crop production.
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References [1] Bakker JC. “Greenhouse climate control : an integrated approach”. (1995), Wageningen Pers. [2] Molina-Aiz FD, Valera DL, Álvarez AJ. “Measurement and simulation of climate inside Almerı́a-type greenhouses using computational fluid dynamics.” Agriculture For Meteorology (2004);125:33–51. doi:10.1016/J.AGRFORMET.2004.03.009. [3] Bournet P-E, Boulard T. “Effect of ventilator configuration on the distributed climate of greenhouses: A review of experimental and CFD studies”. Computers and Electronics in Agriculture (2010);74:195–217. doi:10.1016/J.COMPAG.2010.08.007. [4] Fatnassi H, Boulard T, Bouirden L. “Simulation of climatic conditions in full-scale greenhouse fitted with insect-proof screens”. Agriculture For Meteorology (2003);118:97–111. doi:10.1016/S0168-1923(03)00071-6. [5] Kittas C, Bartzanas T. “Greenhouse microclimate and dehumidification effectiveness under different ventilator configurations”. Building and Environment (2007);42:3774–84. doi:10.1016/J.BUILDENV.2006.06.020. [6] Majdoubi H, Boulard T, Fatnassi H, Bouirden L. “Airflow and microclimate patterns in a one-hectare Canary type greenhouse: An experimental and CFD assisted study”. Agriculture For Meteorology (2009);149:1050–62. doi:10.1016/J.AGRFORMET.2009.01.002. [7] Al-Helal IM, Waheeb SA, Ibrahim AA, Shady MR, Abdel-Ghany AM. “Modified thermal model to predict the natural ventilation of greenhouses”. Energy and Building (2015);99:1–8. doi:10.1016/J.ENBUILD.2015.04.013. [8] Boulard T, Wang S. Experimental and numerical studies on the heterogeneity of crop transpiration in a plastic tunnel. Computers and Electronics in Agriculture (2002);34:173–90. doi:10.1016/S0168-1699(01)00186-7. [9] Chen J, Xu F, Tan D, Shen Z, Zhang L, Ai Q. “A control method for agricultural greenhouses heating based on computational fluid dynamics and energy prediction model”. Applied Energy (2015);141:106–18. doi:10.1016/J.APENERGY.2014.12.026. [10] Miguel AAF. “Transport phenomena through porous screens and openings: from theory to greenhouse practice”. (1998).
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Energy (2019) 000–000 783–790 EnergyProcedia Procedia160 00 (2017) www.elsevier.com/locate/procedia 2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, Sydney, Australia 2nd International Conference on Energy and Power, ICEP2018, 13–15 December 2018, Sydney, Australia
Thermal computational analysis of microclimates for optimal crop production in controlled atmosphere Thermal computational analysis of microclimates forCooling optimal crop The 15th International Symposium on District Heating and production controlled atmosphere a b, Mohammed AbrarinSharief , Ashfaque Chowdhury * Assessing the feasibility of using the heat demand-outdoor a University, Spencer Street, Melbourne, b, Vic 3000, Australia Schoool of Engineering and Technology, CentralSharief Queensland Mohammed Abrar , Ashfaque Chowdhury * Qld 4680, Australia temperature function aQueensland long-term heat demand forecast Schoool of Engineering and Technology,for Central University, district Bryan Jordan Drive, Gladstone, a
b
b
a
a Schoool of Engineering and Technology, Central Queensland University, Spencer Street, Melbourne, Vic 3000, Australia a,b,c a a b c c Schoool of Engineering and Technology, Central Queensland University, Bryan Jordan Drive, Gladstone, Qld 4680, Australia
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
This study aims to perform a thermal computational analysis of the indoor environment with a view to identifying a promising Abstract method to improve the level of harvest in a regulated indoor space or modified version of controlled environment agriculture (CEA) space.study A modified cultivated greenhouse modelanalysis is considered a customary with model. The operational environment of This aims toclosed perform a thermal computational of thebased indooronenvironment a view to identifying a promising Abstract the refined CEA space modelofisharvest simulated computational fluid dynamics environment. In the study, CEA spaceagriculture is assumed(CEA) to be method to improve the level in a in regulated indoor space or modified version of controlled environment heated Abymodified a constant heatcultivated source from the samemodel path for a limited time. space has twoThe air operational inlets on one side and two space. closed greenhouse is considered basedThe on aCEA customary model. environment of District networks are commonly in the literature as one of the most effective solutions for decreasing the outlets on heating the other side. A standard k-epsilon model was implemented in environment. the simulation to define the turbulent transport the refined CEA space model is simulated inaddressed computational fluid dynamics Inmodel the study, CEA space is assumed to be greenhouse gas the emissions from the sector. systems require investments which are returned thetwo heat phenomena. On virtual environment, crop defined usingtime. a porous medium that was proposed byand Darcyheated by a constant heat source frombuilding the the same pathisThese for a limited Thehigh CEA spaceapproach has two air inlets on onethrough side sales. on Due toother theThe changed climate conditions and was building renovation policies, heat model demand in the the future could transport decrease, Forchheimer results are recorded in the form of temperature, velocity and pressure distribution. outlets theLaw. side. A standard k-epsilon model implemented in the simulation to define turbulent prolonging the investment return period. phenomena. On the virtual environment, the crop is defined using a porous medium approach that was proposed by DarcyThe main scope thisresults paper are is torecorded assess the feasibility using the heat demand outdoor distribution. temperature function for heat demand Forchheimer Law.ofThe in the form ofof temperature, velocity and–pressure districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 ©forecast. 2018 TheThe Authors. by Elsevier ©buildings 2019 The Authors. Published by Elsevier Ltd. and typology. that vary in both under construction Three weather scenarios (low, medium, high) and three district This is an open access article the CC period BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) renovation scenarios were developed (shallow, intermediate, deep). To estimate the International error, obtained heat demand values and were Selection andAuthors. peer-review underbyresponsibility of the 2nd Conference on Energy © 2018 The Published Elsevier Ltd. of the scientific committee Selection and peer-review under responsibility of themodel, scientific committee of the and 2ndvalidated International Conference on Energy and compared with results from a dynamic heat demand previously developed by the authors. Power, ICEP2018. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Power, ICEP2018. The results that when weather change considered, the margin of error could be acceptable for some applications Selection andshowed peer-review underonly responsibility of theisscientific committee of the 2nd International Conference on Energy and (the error in annual demand was lower than Analysis, 20% for all weather scenarios considered). However, after introducing renovation Keywords: Crop Production, Microclimate, Thermal Power, ICEP2018. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of Production, slope coefficient increased on Analysis, average within the range of 3.8% up to 8% per decade, that corresponds to the Keywords: Crop Microclimate, Thermal decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. by Elsevier * Corresponding author. Published Tel.: +61411767 886. Ltd. Peer-review under responsibility of the E-mail address: [email protected] Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +61411767 886.
1876-6102 © 2018 The Authors. Published by Elsevier Ltd. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND Selection and peer-review under responsibility of the scientific 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. committee of the 2nd International Conference on Energy and Power, ICEP2018. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Energy and Power, ICEP2018. 10.1016/j.egypro.2019.02.160
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1. Introduction Optimum growth of agricultural product is an incredibly important aspect in the existence of all living lives. The crop production and distribution play a significant role in the economic development of many countries. Therefore, crop production should be maintained as efficiently as practical so that the ever-increasing needs and demands can be fulfilled. Apart from its diverse economy and ecology, Australian farmers are also challenged by the diverse weather conditions. Such variation in weather can sometimes act as a significant problem in the farming because some harvests are very delicate to any changes in atmosphere and thus effects in degradation of the crop quality. Cultivation with the help of greenhouses can be an option to overcome such problems. In the past, greenhouses were just protective glass houses made to trap the sunlight to maintain the indoor temperature. Greenhouses now are equipped with other facilities such as temperature control, light control, carbondioxide control, relative humidity, etc. Adjustments and optimizations are vital in greenhouses to conquer an enhanced plant growth. Numerous resources are put to the test to study the heat transfer properties. Changes in the parameters such as temperature, water vapour, air pressure, air velocity, the rate of radiation etc. have a severe influence on the plant development rates and overall quality of the crops [1]. Therefore, greenhouses or controlled environment agriculture (CEA) spaces are incredibly critical in contemporary agriculture industries. The process of performing a computer simulation of the airflow phenomena in a greenhouse is not only user-friendly but also are very versatile and accurate to understand the controllable factors in the greenhouse. Such an analysis can be performed before the actual greenhouse is constructed or can be applied to an existing greenhouse for retrofitting. The outcome of the study allows the user to identify the optimum design and operating conditions in a cost-effective way. 2. Computational Analysis of Greenhouses Greenhouses are considered as a significant infrastructure which can enhance the crop production and to protect the crop from any forms of dents from weather conditions, pests and rodents etc. Modern CFD programs allow more straightforward investigations of the scalars and vector fields within the greenhouse through the determination of the transport equations of the air that reigns the ventilation [2]. Ventilation is an important consideration during the construction stage of a greenhouse as the ventilation acts as a means of heat and mass transfer to maintain the flow of air into and out of the greenhouse. The adequate level of ventilation helps to dissipate excessive heat, to enhance the exchange of carbon dioxide and oxygen and to maintain acceptable humidity levels since these factors affect the crop development and production. Ventilation is thus a critical process that strongly influences crop production at every stage of the process. Since its very complex to characterize adequate ventilation physically, the computational fluid dynamics (CFDs) procedures are used to map the design and create a better understanding of the air flow and creation of microclimates [3]. It can also be noticed that side openings are less effective than roof openings and adding a side wall may significantly increase greenhouse ventilation [3]. Moreover, ventilation rate strongly decreases as the number of spans increases in a combination of both roof and side wall opening. In a study l2] reported that the CFD method is very effective means of determining the influence of temperature and wind on the crop growth in greenhouses as well as in other viticulture buildings. The researchers applied four different air velocities and found that the ventilation was influenced strongly by the presence of small vent opening area. Insect proof nets have been progressively accepted in protected horticulture industries. Precisely designed superfine meshes are adopted to keep the pests away without the use of harsh chemicals. CFD analysis has been gradually taken to investigate greenhouse aeriation driven by either wind or buoyancy forces. Fatnassi [4] used tomato crop in a largescale greenhouse which was then fitted with the insect-proof screens and was then simulated using the CFD software which analyses water vapour, heat and mass exchanges between the air and the crop, soil temperatures etc. The comparison between measured and computed values for air exchange rate found relatively good accuracy of the model and its ability to create a realistic impression of the greenhouse. The simulations successfully predict the indoor airflow, temperature and humidity with significant temperature and humidity increase with the screen. The results recommended increasing the vent area in order to reserve suitable climatic situations when insect-proof screens are adopted [4].
Mohammed Abrar Sharief et al. / Energy Procedia 160 (2019) 783–790 Mohammed Abrar Sharief et al / Energy Procedia 00 (2018) 000–000
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Natural ventilation is broadly utilized in Greenhouses in Mediterranean climate as such type of ventilation consumes less energy, and spent less money to operate and maintain the setup. High-level humidity during winter influences the crop production due to fungal production and diseases. High humidity causes condensation on plant surfaces which enhances spore growth of certain pathogenic fungi. These fungi reduce in number as the moisture reduces. The high humidity also leads to leaf necrosis, calcium deficiencies and soft, thin leaves which eventually reduce the crop growth [5]. In Almeria province of Spain, a favourite type of greenhouse called Almeria-type is in use where natural ventilation is adopted as the primary climate control method. Natural ventilation consumes less energy, minimum operation and maintenance support and cheaper than other systems to control the indoor temperature. In summer, this type of greenhouse in employed to increase air humidity and to lower the temperatures whereas in winter this type of greenhouse reduces the moisture. The dynamic potency of natural ventilation is the combined effects of wind and buoyancy where its relative importance depends on wind speed and the temperature differences between the inside and outside of the greenhouse. Canary or Parral type of greenhouses are generally extensive (approximate 1 hectare or more), as a result, the side wall ventilation is preferably employed than the roof ventilation [6]. It was reported that Parral type greenhouses fail to consider the interaction among the canopy, the air and the radiative exchangers. When a fine mesh net was employed to avoid the insects, the functioning of ventilation was reduced due to the existence of insect screens. Majdoubi [6] concluded that basic understanding of air exchange mechanisms is essential in determining the suitable climate for the greenhouse air and the canopy. The orientation of crop is also a crucial factor information of a microclimate. Greenhouses require a continuous supply of energy from either renewable or non-renewable sources to maintain the internal microclimate with parameters such as air temperature, wind speed, light intensity etc. Using the CFD technique, Chen [7] investigated the control method integrated with the heat transfer mechanism in a greenhouse considering the impacts of canopy, insect proof screens and heating systems for a perfect microclimate and plant activity. However, heating control strategies for the greenhouse was not adequately considered in the CFD environment; therefore, the optimal design aspects were not completely satisfied in the demand of control system which could improve the indoor temperature conditions and warming efficacy. Al-Helal [8] proposed a thermal model based on pure energy balance applied to a control volume within a greenhouse. The model was applied to predict the natural ventilation rate of any type of greenhouses, for instance, transparent or canopy covered with any shapes and sizes at any locations. The model was simplified by avoiding any uncertain parameters such as average transmittance of the cover and thermal inertia of the soil because they would cause errors in the final results. It was found that the internal and external air temperature are the main parameters controlling the ventilation rate. The model reportedly worked well with zero solar radiation, i.e. at night. This study adopted the mathematical models proposed by Al-Helal [8] to model a modified greenhouse. Thermal computational analysis was also performed, and related parameters were computed in due time. The outcome of the study will assist in determining any change in the settings from the past years and to check if any improvement has been made in the newly analyzed model. 3. Methodology and Numerical Approach To perform the computational analysis, the controlled environment agriculture (CEA) space was first modelled in AutoCAD (Fig.1) and then imported to SimScale, a cloud-based simulation platform, to undertake the CFD analysis. The selected harvest was Australian native tomato which requires a temperature range of 15 deg C to 32 deg C in the CEA atmosphere for yielding at the optimum level. Since a good proportion of tomato consist of water, moisture is considered an essential factor during cultivation. The ideal humidity for tomato crop is between ranges of 80% - 90%. Unwarranted humidity causes suffocation to the crop and may damage the crop heavily. A reduced amount of humidity may reduce the quality of the plant, fail to self-pollinate and grow in the long run. Transpiration is also important in shifting water molecules between the air and the plant. A. Controlled Environment Agriculture Space Characteristics The CEA space is considered as a traditional type with four openings, two on each side. The area of the space is taken as 30 cm in height, 40 cm wide and 51cm in length. The air flow direction related to the sides was in one
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direction. The chosen crop tomatoes were assumed to be sprayed using the drip irrigation technique. The openings are assumed to be fitted with insect-proof screens in the selected controlled environment agriculture space. The CEA space has a total of 4 vents out of which two are present on each side, and the other two are present on the roof.
Fig. 1. The wireframe structure of the controlled environment agriculture (CEA) space
B. System Governing Equations The k- ϵ model is adopted to simulate the airflow in the greenhouse atmosphere. The CFD platform uses standard equations to affect mean flow characteristics, where k is the turbulent kinetic energy and ϵ is turbulent dissipation. The controlled environment agriculture space is modelled using the polyline command functions. The structure is then constructed and extruded in 2 dimensions mode. The main assembly is then hidden to model the rectangular plant structures. The geometry is then imported to SimScale by translating into an Initial Graphics Exchange Specification (IGES) model which ensures better compatibility with the platform. The velocity field, water vapour content and the associated temperature field were determined from the resolution of the popular mass, momentum and energy balance equations using the Boussinesq Hypothesis. The corresponding equations were resolved using the SimScale simulation platform. The presence of crop prompts a drag influence in the controlled space. This is incorporated in the CFD platform using a porous medium. The source termis governed by the Darcy–Forchheimer equation. The crop geometry was considered using the blocks of porous medium with same length and width with the leaf area index, ILA 3. The Darcy– Forchheimer parameters are quantified based on the porosity of the insect-proof net following the relations as given in equations Miguel [9]. The platform allowed to obtain the temperature, pressure and air velocity features in a converged solution. C. Mesh, Grid and Boundary Conditions The computational area considered in the analysis was restricted to the controlled environment agriculture space itself and the free space surrounding it. As shown in Fig. 2 and 3, a hexagonal dominant parametric mesh was created using the default values after importing the geometry into the software, SimScale. Thermal boundary settings were employed to all the walls. The air velocity inlet periphery condition is applied to the two of the openings of the controlled space, and the pressure outlet boundary condition is assigned to the remaining two openings of the same. The doors and other openings were assumed to be closed. The plants were replicated using a Cartesian box boundary condition which is adapted to simulate a porous medium inside the greenhouse. Selected periods of measurements were considered 2 h before and after midday in sunny summer conditions in reasonably steady state conditions with high radiation and ventilation rates. Radiation quantities inside the greenhouse were mainly considered to perform the crop sensibility and latent heat balance.
Mohammed Abrar Sharief et al. / Energy Procedia 160 (2019) 783–790 Mohammed Abrar Sharief et al / Energy Procedia 00 (2018) 000–000
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Fig. 2. The Hex-Dominant Parametric mesh of the controlled environment agriculture space
Fig. 3. The Hex-Dominant Parametric mesh as a wireframe model showing fine structure near plants of the controlled space
4. Results and discussion The controlled atmosphere is considered as heterogeneous where different regions may have different microclimates. In general, the velocity of air reduces as it appears in the space in an enclosed structure, but the air is circulated throughout the enclosed area. Similarly, the pressure reduces as it appears at the centre of the controlled space due to the friction of air-fluid between the walls and plant structures. For simplicity, the air velocity was kept constant at 4 m/s during the simulation. The initial boundary conditions and mesh analysis were performed on a model space and results were recorded. The simulation was then repeated using the controlled space without the harvest and simulation was completed again. The final simulation was conducted in the model by adding the plant structure. The air path was established from the positive x-axis. As shown in Fig 4 and 5, an almost uniform temperature can be observed towards the centre of the greenhouse where the plants are placed. As can be seen in the simulation, the air appears in the space from both the entrances and then the air is circulated throughout the controlled space in Fig. 6. The loss of air velocity and pressure can be viewed due to the friction around the walls. Moreover, high-pressure values can be viewed at the top of the greenhouse as shown in Fig 7. The air escapes from the space via the remaining
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two exits in the direction of the x-axis. The temperature gradually rises near the centre of the controlled space where the plants were placed. The walls of the space remained at the initial temperature. A minor temperature difference was observed between the entrances and exits of the space. The least heated area was the top of the controlled space. The velocity and temperature distribution were found to be homogenous in the centre which is optimum for the growth of the crop. Any losses in the air velocity and momentum could be considered as the losses due to the friction factor of the walls and the plant structures. The results were fairly in agreement with the previous study reported in the literature.
Fig 4. The temperature concentration inside the model space with plants
Fig 5. the temperature concentration in the model space, with plants, in a wireframe structure
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Fig 6. The air velocity and flow direction from both the openings in the model space with plants
Fig 7. The representation of pressure distribution in the model space
5. Conclusions This study assists in observing and studying the airflow phenomena which is useful for designing a new controlled environment agriculture space and/or retrofitting an existing one. The outcome of this simulation-based study is that the temperature was consistent towards the centre of the controlled environment agriculture space, which is usually achieved by using additional heating elements such as fans, hot water pipes etc. It was observed that the air flow circulation was interrupted as soon as it entered the space and decreased gradually which eventually caused a pressure difference. Further investigation is recommended to achieve a better uniformity in the airflow characteristics and enhance the overall efficiency of the controlled environment agriculture space for crop production.
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References [1] Bakker JC. “Greenhouse climate control : an integrated approach”. (1995), Wageningen Pers. [2] Molina-Aiz FD, Valera DL, Álvarez AJ. “Measurement and simulation of climate inside Almerı́a-type greenhouses using computational fluid dynamics.” Agriculture For Meteorology (2004);125:33–51. doi:10.1016/J.AGRFORMET.2004.03.009. [3] Bournet P-E, Boulard T. “Effect of ventilator configuration on the distributed climate of greenhouses: A review of experimental and CFD studies”. Computers and Electronics in Agriculture (2010);74:195–217. doi:10.1016/J.COMPAG.2010.08.007. [4] Fatnassi H, Boulard T, Bouirden L. “Simulation of climatic conditions in full-scale greenhouse fitted with insect-proof screens”. Agriculture For Meteorology (2003);118:97–111. doi:10.1016/S0168-1923(03)00071-6. [5] Kittas C, Bartzanas T. “Greenhouse microclimate and dehumidification effectiveness under different ventilator configurations”. Building and Environment (2007);42:3774–84. doi:10.1016/J.BUILDENV.2006.06.020. [6] Majdoubi H, Boulard T, Fatnassi H, Bouirden L. “Airflow and microclimate patterns in a one-hectare Canary type greenhouse: An experimental and CFD assisted study”. Agriculture For Meteorology (2009);149:1050–62. doi:10.1016/J.AGRFORMET.2009.01.002. [7] Al-Helal IM, Waheeb SA, Ibrahim AA, Shady MR, Abdel-Ghany AM. “Modified thermal model to predict the natural ventilation of greenhouses”. Energy and Building (2015);99:1–8. doi:10.1016/J.ENBUILD.2015.04.013. [8] Boulard T, Wang S. Experimental and numerical studies on the heterogeneity of crop transpiration in a plastic tunnel. Computers and Electronics in Agriculture (2002);34:173–90. doi:10.1016/S0168-1699(01)00186-7. [9] Chen J, Xu F, Tan D, Shen Z, Zhang L, Ai Q. “A control method for agricultural greenhouses heating based on computational fluid dynamics and energy prediction model”. Applied Energy (2015);141:106–18. doi:10.1016/J.APENERGY.2014.12.026. [10] Miguel AAF. “Transport phenomena through porous screens and openings: from theory to greenhouse practice”. (1998).